Blogs

Agentic AI is not a smarter chatbot or a better rules engine. An agentic system receives a goal, breaks it into steps, takes actions across tools and systems to complete those steps, and surfaces results for human review. In a claims context, that means the system moves the claim forward autonomously until it hits a decision that requires human judgment.

When we started mapping where AI could genuinely change delivery outcomes, a pattern emerged fast. Every high-value intervention point.

Across the data platform work we've done, a consistent pattern shows up. Organizations rarely struggle with this because they lack AI tools. They struggle because every newly onboarded entity, whether an acquisition, a partner, or a new customer running its own systems, arrives with different data structures, different governance assumptions, and different operational definitions for things that sound identical. "Active," "completed," and "owner" rarely mean the same thing twice.

We built our first custom software platform in 2009. Since then, we have delivered across healthcare, financial services, insurance, manufacturing, logistics, and verticals that resist easy categorization. The team has grown to over 800 engineers. What has not changed is the map of how projects break.

AI value creation in PE operates at two levels that often get conflated, and conflating them is how operating partners end up evaluating the wrong partner category entirely.